Data driven treatment response assessment and preterm, perinatal, and paediatric image analysis : first International Workshop, DATRA 2018 and third International Workshop, PIPPI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings /: first International Workshop, DATRA 2018 and third International Workshop, PIPPI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings. (2018)
- Record Type:
- Book
- Title:
- Data driven treatment response assessment and preterm, perinatal, and paediatric image analysis : first International Workshop, DATRA 2018 and third International Workshop, PIPPI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings /: first International Workshop, DATRA 2018 and third International Workshop, PIPPI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings. (2018)
- Main Title:
- Data driven treatment response assessment and preterm, perinatal, and paediatric image analysis : first International Workshop, DATRA 2018 and third International Workshop, PIPPI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings
- Other Titles:
- DATRA 2018
PIPPI 2018 - Further Information:
- Note: Andrew Melbourne, Roxane Licandro, Matthew DiFranco, Paolo Rota, Melanie Gau, Martin Kampel, Rosalind Aughwane, Pim Moeskops, Ernst Schwartz, Emma Robinson, Antonios Makropoulos (eds.).
- Editors:
- Melbourne, Andrew
Licandro, Roxane
DiFranco, Matthew
Rota, Paolo
Gau, Melanie
Kampel, Martin
Aughwane, Rosalind
Moeskops, Pim
Schwartz, Ernst
Robinson, Emma
Makropoulos, Antonios - Other Names:
- DATRA (Workshop), 1st
PIPPI (Workshop), 3rd
International Conference on Medical Image Computing and Computer-Assisted Intervention, 21st - Contents:
- DeepCS: Deep Convolutional Neural Network and SVM based Single Image Super-Resolution.- Automatic Segmentation of Thigh Muscle in Longitudinal 3D T1-Weighted Magnetic Resonance (MR) Images.- Detecting Bone Lesions in Multiple Myeloma Patient Using Transfer Learning.- Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response.- Optimizing External Surface Sensor Locations for Respiratory Tumor Motion Prediction.- Segmentation of Fetal Adipose Tissue Using Efficient CNNs for Portable Ultrasound.- Automatic Shadow Detection in 2D Ultrasound Images.- Multi-Channel Groupwise Registration to Construct and Ultrasound-Specific Fetal Brain Atlas.- Investigating Brain Age Deviation in Preterm Infants: A Deep Learning Approach.- Segmentation of Pelvic Vessels in Pediatric MRI Using a Patch-Based Deep Learning Approach.- Multi-View Image Reconstruction: Application to Fetal Ultrasound Compounding.- EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging Without External Trackers.- Better Feature Matching for Placental Panorama Construction.- Combining Deep Learning and Multi-Atlas Label Fusion for Automated Placenta Segmentation from 3DUS.- LSTM Spatial Co-transformer Networks for Registration of 3D Fetal US and MR Brain Images.- Automatic and Efficient Standard Plane Recognition in Fetal Ultrasound Images via Multi-Scale Dense Networks.- Paediatric Liver Segmentation forDeepCS: Deep Convolutional Neural Network and SVM based Single Image Super-Resolution.- Automatic Segmentation of Thigh Muscle in Longitudinal 3D T1-Weighted Magnetic Resonance (MR) Images.- Detecting Bone Lesions in Multiple Myeloma Patient Using Transfer Learning.- Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response.- Optimizing External Surface Sensor Locations for Respiratory Tumor Motion Prediction.- Segmentation of Fetal Adipose Tissue Using Efficient CNNs for Portable Ultrasound.- Automatic Shadow Detection in 2D Ultrasound Images.- Multi-Channel Groupwise Registration to Construct and Ultrasound-Specific Fetal Brain Atlas.- Investigating Brain Age Deviation in Preterm Infants: A Deep Learning Approach.- Segmentation of Pelvic Vessels in Pediatric MRI Using a Patch-Based Deep Learning Approach.- Multi-View Image Reconstruction: Application to Fetal Ultrasound Compounding.- EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging Without External Trackers.- Better Feature Matching for Placental Panorama Construction.- Combining Deep Learning and Multi-Atlas Label Fusion for Automated Placenta Segmentation from 3DUS.- LSTM Spatial Co-transformer Networks for Registration of 3D Fetal US and MR Brain Images.- Automatic and Efficient Standard Plane Recognition in Fetal Ultrasound Images via Multi-Scale Dense Networks.- Paediatric Liver Segmentation for Low-Contrast CT Images. … (more)
- Publisher Details:
- Cham, Switzerland : Springer
- Publication Date:
- 2018
- Extent:
- 1 online resource (xi, 180 pages), illustrations
- Subjects:
- 616.07/57
Computer science
Diagnostic imaging -- Data processing -- Congresses
Computers -- Computer Graphics
Medical -- General
Computers -- Logic Design
Image processing
Health & safety aspects of IT
Algorithms & data structures
Electronic data processing
Computer vision
Medical records_xData processing
Computers -- Computer Science
Program concepts / learning to program
Electronic books - Languages:
- English
- ISBNs:
- 9783030008079
- Related ISBNs:
- 303000807X
9783030008062 - Notes:
- Note: Online resource; title from PDF title page (SpringerLink, viewed September 20, 2018).
- Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.330037
- Ingest File:
- 01_272.xml